Improving social media engagements on paid and non-paid advertisements: a data mining approach Online publication date: Fri, 30-Apr-2021
by Jen-Peng Huang; Genesis Sembiring Depari
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 13, No. 1/2, 2021
Abstract: The purpose of this research is to develop a strategy to improve the number of social media engagement on Facebook both for paid and non-paid publications through a data mining approach. Several Facebook post characteristics were weighted in order to rank the input variables importance. Three machine learning algorithms performance along with dynamic parameters were compared in order to obtain a robust algorithm in assessing the importance of several input factors. Random forest is found as the most powerful algorithm with 79% accuracy and therefore used to analyse the importance of input factors in order to improve the number of engagements of social media posts. Eventually, total page likes (number of page follower) of a company Facebook page are found as the most important factor in order to have more social media engagements both for paid and non-paid publications. We also propose a managerial implication on how to improve the number of engagements in company social media.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Analysis Techniques and Strategies (IJDATS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook